Crystallographic metrology and process control

ABSTRACT

A system ( 70 ) for crystallography including a sample holder ( 74 ), an electron source ( 76 ) for generating an electron beam, and a scanning actuator ( 80 ) for controlling the relative movement between the electron beam and the crystalline sample, the scanning actuator being controllable for directing the electron beam at a series of spaced apart points within the sample area. The system also includes an image processor ( 84 ) for generating crystallographic data based upon electron diffraction from the crystalline sample and for determining whether sufficient data have been acquired to characterize the sample area The system further includes a controller ( 86 ) for controlling the scanning actuator to space the points apart such that acquired data is representative of a different grains within the crystalline sample. IN other embodiments, the invention includes one or more ion beams ( 178, 188 ) for crystallography and a combination ion beam/electron beam ( 218, 228 ).

This application claims the benefit of the Feb. 22, 2002, filing date ofUnited States provisional patent application number 60/359,222incorporated herein by reference.

FIELD OF THE INVENTION

The present invention relates to crystallography, and, moreparticularly, to crystallographic system and methods for inlinemetrology and process control.

BACKGROUND OF THE INVENTION

Some of the primary concerns in the manufacture of semiconductor devicesare the mechanical and electrical properties of the metallization usedto carry electrons within the semiconductor device. As the manufacturingtechnology of semiconductors becomes more sophisticated, the physicalproperties of the materials used in semiconductor device, such as thecomplexity of preferred orientations of polycrystalline microstructures,becomes increasingly important. Crystallographic orientation, grain sizeand grain morphology play major roles in the reliability, qualityassurance, electrical migration resistance, electrical properties,chemical-mechanical polishing (CMP) removal rates, and CMP endpointdetectability. In particular, crystallography plays a role in manyaspects of a CMP process, such as the determination of CMP rate curves,the polishing time, the pressure on the wafer and pad, the speed ofrotation and the slurry feed rate and chemistry, such as polarity. U.S.patent application Ser. No. 10/121,370, incorporated herein byreference, describes adjusting solvent polarities in a CMP slurry tocompensate for differential removal rates.

One of the problems with current manufacturing systems is that processsteps within a semiconductor line are treated as discrete steps, whichare considered to be independent of the step preceding or followingthem. CMP is one such step. It is not generally known in the art thatchemical mechanical planarization is heavily dependent on themorphological and crystallographic nature of the metal put down duringthe deposition process.

The crystallographic microstructure can be examined with a variety oftechniques. Multiphase two-dimensional mapping of crystallographic andmorphological data provides challenges to determine the crystallographicgrain orientation, grain size and grain boundaries of a crystallinesample. There are numerous ways of obtaining this information, but eachof the methods presents slightly different information that the othersdo not.

The processing of materials in the semiconductor industry to achievesmaller geometries introduces new problems as the boundaries betweengrain structures and the orientations of the boundaries become morecritical. For example, the conventional method of indexing Kikuchidiffraction patterns over a scanned area is one method of determiningboth the crystallographic orientation and grain morphology of thin filmson sample surfaces. Backscattering Kikuchi Diffraction (BKD) in ascanning electron microscope (SEM) can produce Kikuchi bands frompolycrystalline grains approaching the size of the probe diameter. Byapplying the rules of point group symmetry to the Kikuchi bands,characteristics such as crystallographic grain orientation and grainsize within a specimen can be determined.

Grains within polycrystalline materials generally have orientations thatvary from grain to grain. This variation, when considered over a bulkspecimen area, can lead to the directional grouping of specificcrystalline planes with respect to certain crystallographic axes. The“preferred orientation” of a polycrystalline sample refers to anaverage, or overall, orientation of the grains. Multiple preferredorientations can also exist simultaneously within a sample. Thecomplexity of the preferred orientation of polycrystallinemicrostructures can be examined with a technique known as OrientationImaging Microscopy, which analyzes collections of BKD patterns. Thistechnique combines the advantages of point orientation in TransmissionElectron Microscopy (TEM) with morphological information over a largeenough area to provide statistical relevance.

For example, aluminum deposited by chemical vapor deposition (CVD)deposits in a preferred orientation along a (111) fiber texture normalto a silicon substrate. This geometry is preferred to reduceelectromigration. BKD pattern analysis can be used to quantify thequality of the deposition of the aluminum along the preferentialcrystallographic axis.

The movement of the semiconductor industry to copper metallization willrequire seed layers and barrier layers made out of tantalum nitride, forexample. The deposition of copper by CVD does not seem to exhibitpreferential orientation. This results in a variable that can differbetween deposited copper films. BKD analysis provides a way ofquantifying the films for orientation analysis in a two-dimensionalmapping array whereby the preferred grain orientations can be comparedfrom one film to another.

BKD pattern analysis works by collecting a Kikuchi pattern at a specificlocation on a sample surface, converting the pattern to a Hough spacewhere each line is represented as a spot, and using the angulardeviations between the spots to calculate the crystallographicorientation of the crystal at that location. The scanning electronmicroscope beam or the sample stage is then stepped to the next pointand the process is repeated. The stepping occurs in a raster patternwith a fixed step size over the entire scan area. Unfortunately, thismethod is very time consuming. For example, to acquire a pattern from anarea that is 10 square micrometers with a step size of 50 nm,approximately 40,000 individual Kikuchi patterns must be collected andanalyzed. With each Kikuchi pattern typically taking approximately 0.5seconds, this yields a scan time for the entire area of approximately 11hours.

The pattern also has a maximum grain boundary resolution of 50 nm. Thelengthy collection time of these patterns makes automated BKD patternanalysis labor intensive and time consuming. Increasing the step sizedoes decrease the time element involved in obtaining and analyzing datewith respect to certain characteristics of a polycrystalline material.

Ion channeling is another technique used, for example, to study defectconcentrations in crystals. When an ion beam is aligned along a majorcrystal axis or plane, ion-atom interaction probability is significantlyreduced, resulting in a large reduction of scattering events and deeperpenetration of ions into the crystal structure. Accordingly, a secondaryelectron signal can be detected and analyzed to determine channellocations, and thus some basic morphology of the crystal, for example,by comparison to a reference crystal. Angle resolved channeling (ARC) ofcrystal planes about different axes can be obtained by adjusting thesample orientation in incremental angular steps. Data is acquired ateach angle and an accumulated data set of backscattered spectra at eachangle is used to create an image of the crystal structure. The maindifficulty with resolving discrete crystallographic information from ionchanneling is due to the overlap in contrast intensities for differentcrystalline orientations.

The foregoing metrological techniques are conducted off-line, i.e., bytaking partially fabricated structures in fabrication, includingsemiconductor devices, out of the manufacturing sequence. However,inline metrology techniques that identify either grain size or preferredorientation of polycrystalline films do not exist. Semiconductor devicesare typically destructively measured offline by time consumingtechniques of electron diffraction and x-ray diffraction. Thedisadvantage of these offline techniques is that they are destructiveand require constant monitoring on test structures and wafers, whichresults in a window between when problems occur and when problems aredetected.

Current micro-electronics manufacturing methods incorporate metrologymethods, such as the methods described above, for the purpose ofdownstream quality control. For example, once a photoresist process hasbeen completed, it is known to utilize a scanning electron microscope orother metrology technique to measure how closely the photoresist maskcorresponds to its intended configuration. A go/no-go parameter may beestablished, and semiconductor wafers having photoresist patterns thatare outside of the acceptance limits are removed from the productionline for subsequent rework. Wafers having acceptable photoresist masksare then processed through a further manufacturing step, such as forexample, an etching process. A second metrology step may then be used toconfirm that the resulting hard mask product falls within predeterminedacceptance limits.

In spite of the numerous advances in micro-electronics manufacturingtechniques, there remain many aspects of various processes that are notfully understood by those skilled in the art. The control of manymicro-electronics manufacturing techniques includes a significant amountof uncertainty. Plasma etch processes are generally difficult tocontrol, with variations occurring from wafer to wafer and from lot tolot. Uncertainties may be induced by machine aging and cleaning leadtimes, run-to-run variations in wafer attributes, and chemistry of theplasma. Quality control is essentially a feed-back process, i.e. theoutput product is measured to determine if it is acceptable, and if itis unacceptable, a control parameter is changed. The output product isthen again measured to see if the desired corrective effect has beenachieved. This cycle is repeated until an acceptable output product isachieved. Each step in the manufacturing process is controlled in asimilar manner. For example, to achieve a desired etch pattern, theremust first be a photoresist development step then an etching step.Current quality control processes involve a first metrology step on thedeveloped photoresist pattern, then a second metrology step on theetched wafer surface. Each of these steps are treated separately, andeach has its own range of acceptable variation from the ideal designvalue. Because these processes are both complicated and not fullyunderstood, there has been no effort in the industry to integrate thequality control aspects of the overall manufacturing process. Such acontrol scheme is naturally rigid, allows for the build-up ofunfavorable tolerances, and provides no capacity for accommodatingdeficiencies in one process with counterbalancing variations in anotherprocess.

It is known to apply a neural network to the control of a semiconductorwafer etching process. Both U.S. Pat. No. 5,653,894 issued to Ibbotson,et al., and U.S. Pat. No. 5,737,496 issued to Frye, et al, describe theuse of neural networks to control the endpoint in a plasma etch process.While such systems provide a degree of in-process control for an etchprocess, further improvements are desired.

SUMMARY OF THE INVENTION

A system for crystallography and process control is described herein asincluding: a sample holder for holding a crystalline sample forcharacterization of a sample area; an electron source for generating anelectron beam; a scanning actuator for controlling the relative movementbetween the electron beam and the crystalline sample, the scanningactuator being controllable for directing the electron beam at a seriesof spaced apart points within the sample area The system also includes:a first processing system for generating crystallographic data basedupon electron diffraction from the crystalline sample; a secondprocessing system configured for determining whether sufficient datahave been acquired to characterize the sample area; and a controller forcontrolling the scanning actuator to space the points apart such thatacquired data is representative of a different grains within thecrystalline sample. The system may also include a first ion source forgenerating a first ion beam; an electron detector for detectingsecondary electrons emitted from the crystalline sample; and aprocessing system for acquiring data based upon secondary electronemissions from the crystalline sample. The system may further include acrystalline standard for providing a channeling contrast reference.

In addition, a system for crystallography is described herein asincluding: a sample holder for holding a crystalline sample; a first ionsource for generating a first ion beam; a scanning actuator forcontrolling the relative movement between the first ion beam and thecrystalline sample, the scanning actuator being controllable fordirecting the first ion beam at desired areas of the crystalline sample;and an electron detector for detecting secondary electrons emitted fromthe crystalline sample. The system also includes: a first processingsystem for creating a contrast intensity image based upon secondaryelectron emissions from the crystalline sample; a second processingsystem programmed to provide crystallographic information based on thecontrast image intensity data; and a controller for controlling thescanning actuator for scanning the first ion beam. The system may alsoinclude a second ion source for generating a second ion beam. The systemmay also include a crystalline standard for providing an ion channelingreference to the processing system.

A method for determining crystallography of bulk crystal sample isdescribed herein as including: providing a sample holder for holding acrystalline sample for characterization of a sample area; generating anelectron beam; controlling the relative movement between the electronbeam and the crystalline sample to direct the electron beam at a seriesof spaced apart points within the sample area. The method also includesgenerating crystallographic data based upon electron diffraction fromthe crystalline sample; determining whether sufficient data have beenacquired to characterize the sample area; and spacing the points apartsuch that acquired data is representative of a different grain withinthe crystalline sample.

A method for determining crystallography of bulk crystal sample isherein described as including: providing a sample holder for holding acrystalline sample; generating a first ion beam; controlling therelative movement between the first ion beam and the crystalline sample,for directing the first ion beam at desired areas of the crystallinesample; detecting secondary electrons emitted from the crystallinesample. The method further includes creating a contrast intensity imagebased upon secondary electron emissions from the crystalline sample;providing crystallographic information based on the contrast imageintensity data; and controlling the scanning actuator for scanning thefirst ion beam.

In addition, a method for determining crystallography of bulk crystalsample is described herein as including: providing a sample holder forholding a crystalline sample; generating a first ion beam; generating anelectron beam; controlling the relative movement between the first ionbeam, the electron beam, and the crystalline sample for directing thefirst ion beam at desired areas of the crystalline sample and fordirecting the electron beam at a series of points within the samplearea. THE method also includes detecting secondary electron emissionsfrom the crystalline sample; creating a contrast intensity image basedupon secondary electron emissions from the crystalline sample andgenerating crystallographic data based upon electron diffraction fromthe crystalline sample; providing crystallographic information based onthe contrast image intensity data and configured for determining whethersufficient data have been acquired to characterize the sample area; andcontrolling the scanning actuator to direct the first ion beam atdesired areas such that each ion channeling image is representative ofchanneling directions within the crystalline sample and spacing thepoints apart such that acquired data is representative of a differentgrains within the crystalline sample.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the invention believed to be novel are specifically setforth in the appended claims. However, the invention itself, both as toits structure and method of operation, may best be understood byreferring to the following description and accompanying drawings.

FIG. 1 is a block diagram illustration of a system for inline metrologyand feed-forward/feedback control of a semiconductor process.

FIG. 2 is a schematic representation of a self-organized neural network.

FIG. 3 is a schematic representation of supervised neural networkarchitecture.

FIG. 4 shows an embodiment of an inline metrology element including anelectron source for scanning a crystalline sample.

FIG. 5 depicts an example schematic scan line (SL) of the prior art.

FIG. 6A depicts an example schematic scan line (SL) the presentinvention (FIG. 4).

FIG. 6B depicts exemplary concentric circle scan lines superimposed overa crystalline sample.

FIG. 7 is a flow chart of the basic steps of a method for scanning acrystalline sample.

FIG. 8 depicts an ion beam embodiment of an inline metrology element forscanning a crystalline sample.

FIG. 9 depicts secondary electron emission and sputtering from a targetmaterial subject to an ion beam.

FIG. 10 shows a graphical representation of the primary channelingdirections of copper and silicon in a random structure, and crystallinestructures having Miller indices of <112>, <100>, and <110>,respectively.

FIG. 11 shows a channeling map for 001 copper.

FIGS. 12A shows a contrast intensity graph plotting tilt angle versusnon-channeling fraction for 001 copper having a Miller index of <110>.

FIGS. 12B shows a contrast intensity graph plotting tilt angle versusnon-channeling fraction for 001 copper having a Miller index of <100>.

FIG. 13A is an FIB image of a nominal aluminum film of approximately 1degree (full width half maximum) FWHM about the times random fibertexture orientation.

FIG. 13B is an electron diffraction image the same aluminum sample ofFIG. 13A.

FIG. 14 shows a pole plot chart for the 111 aluminum sample of FIG. 13A.

FIG. 15 shows pole figure plots for the 111 aluminum sample of FIG. 13A.

FIG. 16A is an FIB image of a defectively processed aluminum film.

FIG. 16B is an electron diffraction image of the same aluminum sample ofFIG. 16A.

FIG. 17 shows a pole plot chart for the aluminum sample of FIG. 16A.

FIG. 18 shows pole figure plots for the aluminum sample of FIG. 16A.

FIG. 19A shows an FIB image for a blanket copper seed layer,

FIG. 19B shows another FIB image of the blanket copper seed layer ofFIG. 19A, wherein the FIB has a different incident angle.

FIG. 20 is an electron diffraction image of the same copper seed layerof FIG. 19A

FIG. 21 shows an FIB image for a partially CMP'd tungsten layer.

FIG. 22 shows an FIB image for a tungsten layer.

FIG. 23 shows an ion beam embodiment of an inline metrology element forscanning a crystalline sample to detect on axis and off axis channelingresponse.

FIG. 24 shows an inline metrology element combining and electron sourceand ion source for scanning a crystalline sample.

FIG. 25 depicts a vertical Bridgman vacuum furnace system for growing abicrystal standard.

FIG. 26 depicts graphite mold having a single nucleation site forgrowing a pure single crystal of random orientation.

FIG. 27 shows a seed crystal that can be extracted at a misorientationangle from a pure single crystal.

FIG. 28 depicts a graphite mold to form a seed grown crystal from a puresingle seed crystal.

FIG. 29 depicts the stages of cutting a seed grown crystal into twobicrystal seeds halves.

FIG. 30 depicts a graphite bicrystal mold for forming a bicrystal frombicrystal seeds halves.

FIG. 31 is a tungsten ion beam channeling contrast image.

FIG. 32A is an inverse pole map of a relatively rough tungsten sampleimaged using electron beam backscatter.

FIG. 32B is an inverse pole map of a relatively smooth tungsten sampleimaged using electron beam backscatter.

FIG. 33A is a reflectivity image of a relatively rough tungsten samplehaving an inset area fraction legend.

FIG. 33B is reflectivity image of a relatively smooth tungsten samplehaving an inset area fraction legend.

FIG. 34 shows an ion channeling contrast image of polycrystallinerecrystallized copper.

FIG. 35 shows an enlarged view of a selected region of the ionchanneling contrast image of FIG. 34.

FIG. 36A, shows an ion channeling image of a copper bicrystal.

FIG. 36B shows an ion channeling image of a copper bicrystal.

DETAILED DESCRIPTION OF THE INVENTION

I. Inline Metrology System

FIG. 1 illustrates a system 10 that may be used to implement a methodfor controlling a semiconductor device fabrication process. In an aspectof the invention, the system provides feed-forward and feedback in-linecontrol of a metal deposition process and of a CMP process based oncrystallography measurements of a processed semiconductor wafer.Advantageously, this system integrates the functions of metaldeposition, inline metrology, and CMP processing to reduce overallprocess time and provide improved control over the device fabricationprocess. As a result, the system can produce an acceptable end productfrom a range of input morphologies that is broader than can otherwise beaccommodated with prior art feed-back systems. In a further aspect tothe invention, neural network algorithms may be provided in the in thefeedback and feed forward loops to adaptively adjust feedback and feedforward data.

As depicted in FIG. 1, a semiconductor wafer is first processed througha metal deposition element 12 to form, for example, a layer of metal onthe semiconductor wafer surface. The metal deposition element 12 may beany known metal deposition development system or method, such as achemical vapor deposition (CVD) process or system. The wafer is thenprocessed through an inline metrology element 14 to measure the geometryand quality of the deposited layer, such as the crystalline structure ofthe layer. The inline metrology element 14 may be any known apparatus ortechnique, and may preferably include developing a crystallographycharacterization of the semiconductor wafer using scanning electronmicroscope (SEM) or a focused ion beam (FIB) inspection data.

In the embodiment of FIG. 1, the inline metrology element 14 may providea multiple parameter characterization of a wafer surface 22 as afeedback signal to the metal deposition element 12, for example tocontrol the deposition, RF power, gas flow and other controllableparameters of the metal deposition process. The inline metrology element14 may access a crystal standard 20 to index measurements performed on awafer to establish an appropriate multiple parameter characterization ofa wafer surface 22. In an aspect of the invention, the multipleparameter characterization of a wafer surface 22 may be provided as aninput to a feedback neural network 24. The feedback neural network 24may be trained by either self-organization or by supervised learning toprovide a feedback control signals 26 to adaptively adjust parameters ofthe metal deposition process. In addition, feedback neural network 24may be provided with historical process data 32 for the depositionprocess. Feedback neural network 24 is designed to find a mapping frommultiple parameter characterization of a wafer surface 22 to parametersto achieve, for example, uniform crystal orientation by deposition ofwafers in the CVD according to a respective crystallography of the waferdetermined in the inline metrology element. A neural network isadvantageously used to define the relationship between the multipleparameter characterization of a wafer surface 22 and, for example, an RFpower used in the CVD process, since the amount of data availableregarding the relationship between these two variables may be limited,and the level of understanding of that relationship may also be limited.As experience is added to the historical database 32 by the processingof wafers through system 10, the precision of this mapping operationwill correspondingly improve.

After inspection in the inline metrology element 14, wafers may then beprocessed in a CMP element 10, for example any known CMP system orprocess. The multiple parameter characterization of a wafer surface 22may be provided in feed forward loop to a CMP process, or in an aspectof the invention, as an input to a feed-forward neural network 28 in aninline metrology feed-forward information path. The feed-forward neuralnetwork 28 then provides a feed-forward control signal 30 to adaptivelyadjust parameters of the CMP process, such as amount of polishingchemical used, removal rate curves, and other adjustable parameters usedin CMP processes. In addition, feedback neural network 24 may beprovided with historical process data 32 for the deposition process.Feed forward neural network 28 is designed to find a mapping frommultiple parameter characterization of a wafer surface 22 to parametersto achieve, for example uniform polishing of wafers according to arespective crystallography of the wafer determined in the inlinemetrology element. A neural network is advantageously used to define therelationship between the multiple parameter characterization of a wafersurface 22 and, for example, CMP removal rate curves, since the amountof data available regarding the relationship between these two variablesmay be limited, and the level of understanding of that relationship mayalso be limited. As experience is added to the historical database 32 bythe processing of wafers through system 10, the precision of thismapping operation will correspondingly improve.

Neural networks 24, 28 may be trained by any method known in the art,including by a self-organized learning algorithm of the type describedin Kohonen, T., Self-organization and Associative Memory,Springer-Verlag, Berlin (1984). The Kohonen algorithm is just one ofmany self-organization algorithms (also known as competitive learning)that have been reviewed by Ballard, D. H. (1997), An Introduction toNatural Computation, MIT Press, Cambridge, Mass. and by Hassoun, M. H.(1995), Fundamentals of Artificial Neural Networks, MIT Press,Cambridge, Mass. A self-organized network 40 having input nodes 42 isillustrated in FIG. 2 and may be trained as follows. Assume the inputvector is the multiple parameter characterization of a wafer surface 22.Let this vector be represented by the symbol M and let the output fromthe classification nodes 44 be represented by the symbol Y, and W willrepresent the connection matrix 46 between the input 42 and theclassification nodes 44. The value for any given element in the Y vectoris given by: $\begin{matrix}{y_{j} = {f( {\sum\limits_{i}{W_{ij}x_{i}}} )}} & \lbrack 1\rbrack\end{matrix}$If one starts with small positive random numbers in the connectionmatrix W, then each classifier node will produce the same output valuefor the same input vector. The function f could be a simple logisticfunction, for example: $\begin{matrix}{{f(z)} = \frac{1}{( {1 + {\exp( {- z} )}} )}} & \lbrack 2\rbrack\end{matrix}$With this algorithm one of the output vector elements will have agreater value than the others (e.g. ^(y) ¹ ^(<y) ² ^(y) ¹ ^(<y) ³ ^(y) ²^(>y) ³ ). One may exploit the differences between the individual outputvector elements for computational purposes by increasing or decreasingtheir values. In the example described, the second element is thelargest value or the winner. But it is not necessary to determineexplicitly which value is the largest or which is the smallest. One maysimply use the following algorithm to update the weights between theinput nodes and the classification nodes.^(W) ^(ij) ^((t+1)=W) ^(ij) ^((t)+δW) ^(ij) ^(x) ^(i)   [3]The advantage of this algorithm is that there is no need to explicitlyexamine the classifier output. To determine if the networkself-organization has converged, one may simply monitor the magnitude ofthe changes taking place in the connection matrix 46.

After training the network, one may find that the actual outputs for theclassifier nodes are not as strong as preferred, but that they aredifferentiated from each other. It is possible to further enhance thisdifference by using a winner-take-all network 48 on the back-end of theself-organizing classifier. This can be done by using simple “If”statements to select the largest output, or by a MAX(y) function toarrive at output nodes 50. At this stage, the system 10 of FIG. 1 willprovide a goodness class from the network and this represents, forexample, the quality of the multiple parameter characterization of awafer surface 22

There may be and preferably are similarities in training the feedbackneural network 24 and the feed-forward neural network 28. A supervisedlearning algorithm known as the back-propagation of errors can trainboth. This algorithm is well known in the art with many published papersdescribing it and its applications. Two published sources for thealgorithm are Hassoun, M. H., Fundamentals of Artificial NeuralNetworks, MIT Press, Cambridge, Mass. (1995) and Reed, R. D. and Marks,II, R. J., Neural Smithing—Supervised Learning in Feedforward ArtificialNeural Networks, MIT Press, Cambridge (1999). The following is a briefdescription of the training algorithm and how it may be applied to thepresent invention, as illustrated in FIG. 3.

The output of a neural network, r, is given by $\begin{matrix}{r_{k} = {\sum\limits_{j}{\lbrack {{W_{jk} \cdot \tan}\quad{h( {\sum\limits_{i}{W_{ij} \cdot x_{i}}} )}} \rbrack.}}} & \lbrack 4\rbrack\end{matrix}$This equation states that the i^(th) element of the input vector x ismultiplied by the connection weights W_(ij). This product is then theargument for a hyperbolic tangent function, which results in anothervector. The resulting vector is multiplied by another set of connectionweights W_(jk). The subscript i spans the input space. The subscript jspans the space of “hidden nodes”, and the subscript k spans the outputspace. The connection weights are elements of matrices, and are found bygradient search of the error space with respect to the matrix elements.The cost function for the minimization of the output response error isgiven by: $\begin{matrix}{C = {\lbrack {\sum\limits_{j}( {t - r} )^{2}} \rbrack^{\frac{1}{2}} + {\gamma{W}^{2}}}} & \lbrack 5\rbrack\end{matrix}$The first term represents the RMS error between the target t and theresponse r. The second term is a constraint that minimizes the magnitudeof the connection weights, W. If γ (called the regularizationcoefficient) is large, it will force the weights to take on smallmagnitude values. This can cause the output response to have a lowvariance, and the model to take on a linear behavior. With this weightconstraint, the cost function will try to minimize the error and forcethis error to the best optimal between all the training examples. Theeffect is to strongly bias the network. The coefficient γ thus acts asan adjustable parameter for the desired degree of the non-linearity inthe model.

In order to apply this technique to training the feed forward neuralnetwork 28, for example, we use the multiple parameter characterizationof a wafer surface 22 as the input vector X and a CMP removal ratecurve, for example, as the output target value T. This discussion isprovided for illustration only, and does not limit the application ofthis invention to other control parameters used in a CMP or CVD process.Similarly, the method described herein may be applied directly to otherprocesses, such as an etch processes or photolithography processes.After selecting the input vector X and the target value T, the network28 is then trained using the previously described well known algorithmand/or using the historical process data 32. The objective of network 28is to compute a CMP removal rate curve for running a CMP process. Theinput to the neural network 28 is the multiple parametercharacterization of a wafer surface 22 and the network 28 will model theprocess of mapping the multiple parameter characterization of a wafersurface 22 to the CMP removal rate curve. As additional data isaccumulated, the connections in neural network 28 can be updated as theprocess changes, thereby helping neural network 28 adapt to real-worldchanges.

Accordingly, system 10 may be used for on-line automation of asemiconductor manufacturing process allowing for the selection of acontrol parameter values that will move a measured wafer geometry towarda target acceptance range. Such feed-forward and feedback controlapplies information acquired through in-process metrology to adownstream and an upstream manufacturing step, respectively. In anembodiment, the feed-forward and feedback controls are provided to therespective manufacturing steps via neural networks having as an input amultiple parameter characterization of the input device morphology. Thisintegrated metal deposition-inline metrology-CMP process can adapt tochanges in variables that may not be adequately monitored or whoseimpact may not be fully appreciated to adjust the process in response tothe input profile and the desired after-etch profile.

II. Inline Metrology Elements

As described earlier, the inline metrology element 14 may include anyknown apparatus or technique, or combinations of apparatuses oftechniques, and may include developing a crystallographycharacterization of the semiconductor wafer using scanning electronmicroscope (SEM) or a focused ion beam (FIB) inspection data.Advantageously, the invention provides a novel inline metrology systemand method that measures crystallographic orientation, grain size, andgrain morphology at faster speeds than conventional possible without theneed for offline and/or destructive testing. As used herein, areafraction means the fractional percentage of a grain orientation along apolycrystalline material surface relative to a specified direction.

A. Scanning Electron Microscope (SEM)

Referring now to FIG. 4, an embodiment of an inline metrology element 70for scanning a crystalline sample 72, such as a copper film deposited ona semiconductor wafer, will now be described. The metrology element 70includes a sample holder 74 for holding the sample 72 at a glancingangle θ to an electron beam 78. The electron beam 78 is generated by anelectron source 76. A scanning actuator 80 is provided for controllingrelative movement between the electron beam 78 and the crystallinesample 72 on the sample holder 74. The scanning actuator 8 iscontrollable for directing the electron beam 78 at a series of spacedapart points of the crystalline sample 72. In other words, the scanningactuator 80 may control movement of the electron source 76 to move theelectron beam 78 relative to the sample 72 on the sample holder 74, orthe scanning actuator may control movement of the sample holder relativeto the electron beam, or both.

An processing system, such as an image processor 84, is provided toprocess images formed on a phosphor screen 82, e.g., by intensifyingand/or amplifying the images. The image processor 84 may comprise a lowlight or charged coupled device (CCD) camera 88 to capture the images.The phosphor screen 82 is mounted adjacent the sample holder 74 so thatit is parallel to the incident electron beam 78. Diffracted electronsfrom the sample 72 form images on the phosphor screen 82. These imagesare known as Kikuchi diffraction patterns and include Kikuchi bands,which can be used to determine the crystallographic grain orientation ata point within a scan area of the sample 72. The pattern center ispreferably located near the top of the phosphor screen 82 for maximumband formation.

The image processor 84 mathematically decomposes the Kikuchi diffractionpattern through a Hough transform to identify the band structure, as iswell known to those skilled in the art. See, for example, U.S. Pat. No.6,326,619. The geometrical symmetry of the band structure is used todetermine the crystallographic grain orientation of the crystallinesample at the current point. A controller 86 compares thecrystallographic grain orientation at the current point with thecrystallographic grain orientation from a previous point. The electronbeam 78 or the sample holder 74 is then stepped to the next point andthe process is repeated. The stepping occurs in a raster pattern with apredetermined step size over the entire scan area.

The crystallographic grain orientation of a crystal phase varies withina narrow tolerance. This tolerance is typically less than the noiseexhibited by the Hough transformation conversion to angular spacingbetween crystal planes. Therefore, only a single determination of thecrystallographic grain orientation is needed for each point. In oneaspect, the step size or spacing between sample points is set such thateach point is taken from a different grain within a scan area of thecrystalline sample 72. The term “step size,” as used in the disclosureis the distance between consecutive points of a sample at which theelectron beam 18 is directed for grain orientation analysis.

The step size may be greater than a “known grain size” of thecrystalline sample and/or at least as large as a “known grain size.” Theterm “grain size,” as used in this disclosure, refers to thatmeasurement of a grain using techniques known to those skilled in theart, e.g., an intercept method (ASTM Test Method E 112) or planimetricmethod (ASTM Test Method E-2) or other methods. For a description ofsuch test methods, see Vander Voort, “Committee E-4 and Grain SizeMeasurements: 75 Years of Progress,” ASTM Standardization News (May,1991).

The known grain size may be characterized as a standardized grain sizefor a particular crystal phase of the crystalline and may be obtainedfrom publications listing standardized grain size for various materials.One such publication is The Journal of Vacuum Science and Technology.The step size for operation of the present invention is a function ofgrain size, such as ten times the grain size.

Referring to FIGS. 5 and 6A, example schematic scan lines (SL) of theprior art (FIG. 5) and the present invention (FIG. 6A) are now compared.Each of the scan lines depicted in FIGS. 5 and 6 is schematicallyrepresented as a straight line. The scan lines disclosed in FIGS. 5 and6A herein represent a series of spaced apart points taken from a sampleand may follow any preselected pattern, a random pattern, and may haveany desired spacing distance. In the present invention, for example, afirst point may be randomly selected within a scan area of the sample72; and the next point may be spaced apart at a distance that is atleast as large as a known grain size of the sample 72. A preselecteddirection with respect to consecutive points is not critical to theoperation of the present invention, but it is preferred to obtain grainorientations of different grains of the sample within a scan area.

The prior art scan line SL of FIG. 5 includes spaced-apart points P1-PNwhere data is taken. The points P1-PN are spaced apart by a fixed stepsize S, e.g., 50 nanometers. Grain boundaries (GB) exist within thissample scan line SL and, as illustrated, the number of data points P1-PNis fixed, based on the fixed step size S.

The scan line SL illustrated in FIG. 6A, according to the presentinvention, includes spaced apart points P1 through P7 where data istaken. Grain boundaries (GB) exist between grains (G1 through G7),within the sample scan line SL, but the spacing between points isincreased to reflect a point taken from a different grain within a scanarea of the sample 72. For example, the spacing between each of thepoints P1 through P7 is twice the size of the grains G1-G7. In thismanner, a grain orientation analysis can be taken from a different grainwithin the scan line for each given point.

Referring to FIG. 7, the basic steps of the method for scanning acrystalline sample 72 in accordance with the present invention are nowdescribed. In accordance with the present invention, the method begins(Block 90) and the sample 72, e.g., a copper film deposited on asemiconductor wafer, is held at a glancing angle to an electron beam 78.For example, the sample 72 may be held by a sample holder 74 or stage,as shown in FIG. 4, in a substantially horizontal position. Preferably,the glancing angle θ is about 20 degrees. At Block 92, an electron beam78 is generated at the sample 72 and at Block 94, the relative movementbetween the sample 72 and the Block 94 is controlled to direct theelectron beam at a series of spaced apart points of the crystallinesample.

Diffracted electrons from the crystalline sample 72 define an image,which is processed (Block 96), e.g., by an image processor whichpreferably includes a low light camera or a CCD camera. The image may beformed on a phosphor screen 82 and comprises a Kikuchi diffractionpattern. The step of processing the image (Block 96) may includeconverting the Kikuchi diffraction pattern to a Hough space to identifythe Kikuchi bands at a current point within the crystalline sample 72.Also, the step of processing the image (Block 96) may includedetermining a crystallographic grain orientation at the current pointwithin the crystalline sample 72 based on the Kikuchi bands.

The method preferably includes a step (Block 98) of comparing thecrystallographic grain orientation from the current point with thecrystallographic grain orientation from a previous point. At Block 100an average grain orientation between the current point and the previouspoint is determined. For example, with respect to FIG. 5, if the grainorientation of P1 is (1,1,1) and the grain orientation of P2 is (0,0,1),the average grain orientation is the vector that bisects the directionvectors of the (1,1,1) and (0,0,1) planes.

At block 102, if the average grain orientation of only two points hasbeen taken, then the crystallographic grain orientation for a thirdpoint must be determined. Once a grain orientation for a third point, P3(FIG. 6) is taken, it is compared to the grain orientation of points P1and P2. At Block 100, an average grain orientation for points P1, P2 andP3 is determined to be, for example. In the next step (Block 104), thecontroller 96 determines a variance between average grain orientationfor points P1-P2 and an average grain orientation calculated for pointsP1, P2 and P3.

At Block 104 a variance between the average grain orientation determinedfor points P1 and P2 is compared to the average grain orientation forpoints P1, P2 and P3, to determine a variance in average grainorientation. The term “variance,” as used in this disclosure, shallinclude a variance or standard deviation in average grain orientations,which variance and/or standard deviations are calculated from astatistical analysis of the data comparing grain orientations and/oraverage grain orientations. The mathematical formulas are well known tothose skilled in the art.

In the next step (Block 106), the variance in the average grainorientation of the sample 72 is compared to a predetermined value. Ifthe variance approaches, or is equal to, the predetermined value thescanning is terminated at Block 108. If the variance is not approaching,or equal, to the predetermined value the scanning of the sample 72continues to the next point. The scanning process is repeated for pointsP4 and P5. The variance in grain orientation is monitored during thescanning procedure, or until the variance approaches, or is equal to,the predetermined value, at which step (Block 108), the scanning isterminated. Typically, the predetermined value will be zero.

A benefit of the method of the present invention is that is providesfaster collection. In accordance with the method, the number of datapoints within a grain structure can be decreased where points taken at astep size, for example, larger than a known grain size of thecrystalline sample is needed. An adequate sample of data is collectedfor evaluation of the crystalline sample, in a minimal amount of time.With respect to fabrication of semiconductor devices, thecrystallographic grain orientation of each of respective scan points canbe used to calculate a preferred grain orientation of the sample, whichcalculation is well known to those skilled in the art.

FIG. 6B depicts exemplary concentric circle scan lines superimposed overa crystalline sample. In another aspect of the invention, a statisticalmethod of determining grain size based upon optical methods may be usedby adapting it to the electron beam tool and applying a scan methodwhereby the beam is directed to move in either concentric circles or aseries of lines and the grain boundaries counted. The ratio of thenumber of grain boundaries to the size of the concentric circles orlength of the lines can be used to make a statistical determination ofgrain size. The novel aspect of this invention is in utilizing theelectron beam tool with a step size small enough to be able todistinguish grain boundary locations along a series of circles or lines.The mathematical methods for determining the statistical grain size fromthe number of events is known to one skilled in the art.

Accordingly, an electron beam system using variable scan methods canprovide statistical orientation and statistical grain size of acrystalline sample. Because of the greatly reduced time frame forcollection, this method can be used inline to determine grain size andcrystallographic information.

B. Single Focused Electron Beam (FIB)

Referring now to FIG. 8, an ion beam embodiment of an inline metrologyelement 110 for scanning a crystalline sample 112, will now bedescribed. The metrology element 110 includes a sample holder 114 forholding the sample 112 at an angle θ to an ion beam 118. In an aspect ofthe invention, the angle is 90 degrees from the face of the crystallinesample 112 to be aligned with the fiber texture orientation so thatcrystals of similar orientation are detected with similar intensities.The ion beam 118 is generated by an ion source 16, such as a focused ionbeam (FIB). In another aspect of the invention, the angle θ can beapproximately 85 degrees from the face of the crystalline sample 112 sothat the an ion beam 118 is not aligned with the fiber textureorientation. In this angular configuration, crystals of similarorientations are detected with varying intensities so that theboundaries can be identified.

A scanning actuator 124 is provided for controlling relative movementbetween the ion beam 118 and the crystalline sample 112 on the sampleholder 114. The scanning actuator 124 is controllable for directing theion beam 118 at selected areas of the crystalline sample 112. In otherwords, the scanning actuator 124 may control movement of the ion source116 to move the ion beam 118 relative to the sample 112 on the sampleholder 114, or the scanning actuator may control movement of the sampleholder relative to the electron beam, or both. An electron detector 120is provided to detect electrons 126 emitted from the surface of thesample 112 as the ion source 116 scans the ion beam 118 across thesample 112. In an aspect of the invention, the electron detector 120 ismounted perpendicular to the ion beam 118 near the edge of sample asshown in FIG. 8.

An image processor 121 may be provided to process contrast imagesreceived by the electron detector 120, e.g., by intensifying and/oramplifying the images. Once an image has been processed for a specificarea, a controller 122 provides a control signal to the scanningactuator 124 to move the ion beam 118, or the sample holder, 114 toanother desired area of the sample 112.

As is known in the art, electron emission from a crystalline materialvaries according to the statistical likelihood of a collision betweenand incident ion and a nucleus of a sample surface. The closer thedistance to the surface where the nucleus impact occurs, the moreintense the emission of secondary electrons from the surface. In acrystalline material , the likelihood for collision near the surface isreduced along, aligned areas of the crystal structure, or channelingdirections. That is, the ion beam penetrates further in a channelingdirection than a non-channeling direction.

An FIB instrument utilizes a finely focused ion beam, for example, froma Ga liquid metal ion source (LMIS) to perform imaging operations. Theinteraction of the finely focused ion beam with the target material willproduce the ejection of secondary electrons, secondary ions, andsecondary neutrals. The ions and neutrals can be ejected as individualatoms, molecules, or clusters. The imaging capability of the FIB allowsthe use of either the secondary electrons or the secondary ions forimage formation.

FIG. 9 depicts secondary electron emission and sputtering from a targetmaterial subject to an ion beam. It is well known that an incident ion130, upon impact with a target material 132, will produce a collisioncascade in the target material 132. If a surface atom receives enough ofa normal component of momentum from the collision cascade to overcome asurface binding energy of the target material 132, surface atoms, suchas a sputtered particle 138 leave a surface 134 of the target material132. The factors that affect sputtering include the atomic number,energy, and angle of incidence of the ion beam, the atomic density ofthe target material 132, surface binding energy of the target material132, and crystallographic orientation of the target material 132. Inaddition, the incident ion 130 will also generate secondary electron 136emission form the surface of the target material 132. This secondaryelectron 136 emission is a function of the depth of penetration of theincident ion 130. Therefore it is possible to determine the approximatedepth of penetration of an incident ion by examining the intensity ofthe generated secondary electrons 136 being emitted from the samplesurface.

FIG. 10 shows a graphical representation of the primary channelingdirections of copper and silicon in a random structure, and crystallinestructures having Miller indices of <112>, <100>, and <110>,respectively. The incident ions statistical depth is a function of thepacking density of the cross sectional area of the depth of penetration.Therefore, for axial channeling directions where there are wider atomicchannels, the primary ion will penetrate deeper into the sample surfaceand therefore generate less secondary electron emission from the top ofa sample surface. For directions not coincident with channelingdirections, there is a greater statistical probability of the collisionof an incident ion with the nucleus of the sample surface and therefore,there will be greater amount of electrons emitted for sample directionsnot coincident with the channeling direction of the sample surface.

FIG. 11 shows a channeling map for 001 copper. The map depicts theeffective channel widths for a primary direction and secondary directionwithin the copper. Analysis along a particular direction of thechanneling map of FIG. 11 provides the contrast intensity graphs ofFIGS. 12A and 12B. Accordingly, FIGS. 12A and 12B show contrastintensity graphs plotting tilt angle versus non-channeling fraction for001 copper having a Miller indices of <110> and <100>, respectively.

Therefore, because of the crystal channeling, the FIB can be used toproduce ion channeling contrast plots from secondary electron imagescaptured by an electron detector recording the intensity of thesecondary electron emission in polycrystalline samples. Ion channelingcontrast occurs because the secondary electron yield varies as afunction of crystallographic orientation within the sample. Channelingcan occur when a crystallographic axis of a particular grain is alignedwith the incident ion beam. As a result, that grain will appear darkerdue to a decrease in the number of secondary electrons that areproduced. Advantageously, the ion channeling contrast can be interpretedto determine the orientation of the crystals in the a sample.

As the ion beam 118 is scanned across the surface to the sample 112,relatively fewer electrons will be emitted from the sample 112 when theion beam 118 intersects a channeling direction, and a relatively greaternumber of electrons will be emitted when the ion beam intersectsnon-channeling directions. Therefore, both the orientation of the sample112 with respect to the incident ion beam 118 as well as the channelingdirections in the sample 112 affect the emission of electrons 126 fromthe sample 112 surface.

Accordingly, as the electron detector 120 detects the electrons 126emitted from the sample 112, the emission intensity is provided to thecontroller 122 to correlate the emission intensity with a targetedlocation on the sample 112. Using the corrected emission intensity andtargeted location information, a contrast map of the sample's 112corresponding to crystalline channeling areas in the sample 112 can becreated. The resulting contrast map can be used to determine thecrystallographic grain orientation at a point within a scan area of thesample 112. In an embodiment of the invention, the contrast mapdeveloped using an FIB, for example, can be used to monitor processesand provide process control by taking advantage of the fiber texturenature of semiconductor metals.

To demonstrate the capabilities of ion channeling contrast indetermining crystallographic orientation, several examples are providedherein. In addition, corresponding comparisons to known SEM techniquesfor determining crystallographic orientation is also provide forvalidation of the process.

FIG. 13A is an FIB image of a nominal aluminum film of approximately 1degree (fill width half maximum) FWHM about the times random fibertexture orientation, the image having a scale of 10 micrometers.Typically aluminum has strong fiber textural orientation of 111.Therefore, the primary direction of <111> is noted and changes inorientation are reflected as movements away from the channelingdirection of 111, that is, the image shows more contrast away from achanneling direction due to a larger likelihood of collision between anincident ion and nuclei of the sample surface. The image appears to haveall the same contrast except for certain areas which appear slightlybrighter, These areas are the grain boundaries where the disorder at theboundary has created a localized increased likelihood of nuclearcollision using, for example, an FIB.

FIG. 13B is an electron diffraction image the same aluminum sample ofFIG. 13A, the image having a scale of 5 micrometers. Inset in the lowerright corner of the image is an inverse pole figure legend 142 whereinthe gray shading corresponds to an automatic tiling function, as knownin the art, of the unit triangle of the inverse pole figure. Forexample, the upper right portion of the inverse pole figure legend 142is assigned a <111> crystal direction, the lower right portion isassigned a <101> crystal direction and the lower right portion isassigned a <001> crystal direction. Accordingly the gray scaling of theimage corresponds to crystal direction according to the defineddirections of the inverse pole figure legend 142. As can be seen,virtually the entire image corresponds to a crystal direction of <111>.

FIG. 14 shows a pole plot chart for the <111> aluminum sample of FIG.13A. The pole plot chart can be generated from the electron diffractioninformation to analyze the times random component of the <111>orientation of the aluminum sample. The pole plot shows both therandomness of the sample in along rolling and transverse directions, thetwo directions in the plane of the wafer, and the relative strength ofhe fiber texture orientations. For example, the pole to be plotted isspecified by entering the hkl Miller index for the desired lattice planeof interest. From the pole plot, it is evident from the steep drop offfrom the 0 degree angle axis that the imaged aluminum sample exhibitsextremely tight textural orientation.

The pole plot also shows a the position of a pole (a normal to thelattice plane) relative to a sample reference plane. For example, thepole to be plotted is specified by entering the hid miller index for thedesired lattice plane of interest. The pole figures are a means ofidentifying preferred orientations within a polycrystalline sample. Asshown, a tight, well formed circle in the <111> direction pole figureplot 144 indicates a strong orientation in the <111> direction.

FIG. 16A is an FIB image of a defectively processed aluminum film, theimage having a scale of 10 micrometers. The process used to treat thealuminum film omitted a barrier step resulting in a weaker fiber textureof the defectively processed sample. Accordingly, the image exhibits anoticeable contrast differential caused by the weaker fiber texture.Advantageously this demonstrates that the FIB imaging process can detectproblems caused by mistakes in barrier steps as well a metallizationsteps.

FIG. 16B is an electron diffraction image of the same aluminum sample ofFIG. 16A, the image having a scale of 5 micrometers, and including aninset inverse pole figure legend 146. The diffraction image exhibitscrystal asymmetries as indicated by the lighter colored areas 148 in theimages, thereby validating the FIB image obtained.

FIG. 17 shows a pole plot chart for the aluminum sample of FIG. 16A. Thepole plot chart shows a far weaker fiber textural orientation asexhibited by the spread of the poles away from a 0 degree anglereference. This weakened fiber textural orientation can result inlowered mean time failure (MTF) rates in aluminum metallization.

FIG. 18 shows pole figure plots for the aluminum sample of FIG. 16A. Asshown, misshapen, loose diffraction circles in the pole figure plotsfurther indicate a weak fiber orientation in the aluminum sample of FIG.16A. Accordingly, the electron backscatter diffraction data validatesthat the orientation of fiber textural material can be demonstratedusing FIB channeling contrast images.

Turning now to an example of a copper seed layer, FIG. 19A shows an FIBimage for a blanket copper seed layer, the image having a scale of 20micrometers. For copper seed layers, it is known that there iselectromigration resistance for <111> copper (Cu) as compared to otherorientation normals. The FIB image of FIG. 19A comprises primarily grayareas indicating a <111> orientation in the copper seed layer. Thesmaller areas of increased whiteness or blackness within the image are“three sigma” annealing twins of the <111> Cu. The two darker grains150, 152 near the bottom of the image are grains having an appreciablydifferent orientation than the rest of the sample.

FIG. 19B shows another FIB image of the blanket copper seed layer ofFIG. 19A, wherein the FIB has a different incident angle. As shown, byusing a an incident ion angle that is not coincident with the centralaxis of the fiber texture of an imaged sample, the grain size can beeasily identified and calculated.

FIG. 20 is an electron diffraction image of the same copper seed layerof FIG. 9A, the image having a scale of 4.5 micrometers. The electrondiffraction image reveal a mostly <111> orientation, as expected, withsome grains having an off-axis orientation. The bulk of the copper seedlayer having an <111> orientation is imaged as black, while the threesigma boundaries that are the annealing twins of the <111> Cu are imagedas lighter areas.

Turing now to a comparison of tungsten imaging using FIB and SEMtechniques, FIG. 21 shows an FIB image for a partially CMP'd tungstenlayer, the image having a scale of 5 micrometers. The darker areas 154,156 correspond to areas of 110 orientation, while the overall lighterareas 158, 160 correspond to 114 orientation, as verified by electronbackscatter diffraction. Of particular note for a CMP process, the 114orientation polishes two to four times faster than the 110 orientation.FIG. 22 shows an FIB image for a tungsten layer, the image having ascale of 10 micrometers. In this image, lighter areas (114 orientation)can easily be seen to dominate over the darker areas (110 orientation).It is known that one of the primary causes of the variation in CMPremoval rate and the failure of some of the wafer lots with respect toendpoint detection due ratio of orientation components is thedrastically differential oxidation reaction rates of orientation faces,such as the 114 and 110 orientations of the tungsten crystal shown inFIG. 22. The novel FIB technique described above provides the ability toquantify the area fraction of the respective orientation components andfrom the quantification, determine the required CMP removal ratedirectly. The FIB technique also allows monitoring the wafers atdeposition to determine the stability of the process, for example ifcrystals are being deposited in a desired orientation.

Accordingly, ion channeling contrast images can be used to determinesingle fiber textural strength and dual fiber texture strength and areafraction. Because ion channeling is responsive to depth in a crystallinestructure, area fraction and crystallographic deviations versus depth ina metallic film, for example, may be determined. For example, bydetermining the crystallographic makeup for a particular area of thesample at varying depths, a three dimensional reconstruction of thesample surface crystallography may be made. Using information regardingcrystal orientation data, reconstruction of CMP removal rate curves canbe accomplished for more efficient CMP of an inline imaged sample.

C. Dual FIB

Referring now to FIG. 23, an ion beam embodiment of an inline metrologyelement 170 for scanning a crystalline sample 172 for on axis and offaxis channeling response, will now be described. The metrology element170 includes a sample holder 174 for holding the sample 172 at aglancing angle θ1, such as an on axis channeling angle, to an ion beam178, and at a second glancing angle θ2, such as an off axis channelingangle, to an ion beam 188. In an aspect of the invention, the glancingangle θ1 is 90 degrees from the face of the crystalline sample 172,while the glancing angle θ2 can be varied from 0 to 90 degrees. The ionbeams 178, 188 are generated by respective ion sources 176, 190, such asFIB sources. A scanning actuator 184 is provided for controllingrelative movement between the ion beams 178, 188 and the crystallinesample 172 on the sample holder 174. The scanning actuator 184 iscontrollable for directing the ion beams 178, 188 at selected areas ofthe crystalline sample 172. In other words, the scanning actuator 184may control movement of the ion sources 176, 190 to move the ion beams178, 188 relative to the sample 172 on the sample holder 174, or thescanning actuator 184 may control movement of the sample holder 174relative to the ion beams 178, 188, or both. In an aspect of theinvention, the ion beams 178, 188 are positioned to aim at same spot onthe sample 172 to provide an on axis and off axis channeling responsefor the selected spot. An electron detector 180 is provided to detectelectrons 186 emitted from the surface of the sample 172 as the ionsources 176, 190 scan the respective ion beams 178, 188 across thesample 172. In an aspect of the invention, the electron is mountedperpendicular to the ion beam 178 near the edge of sample as shown inFIG. 23.

An image processor 181 may be provided to process contrast imagesreceived by the electron detector 180, e.g., by intensifying and/oramplifying the images. Once an image has been processed for a specificarea, a controller 182 provides a control signal to the scanningactuator 184 to move the ion beams 178, 188, or the sample holder 174,to another desired area of the sample 172. In an aspect of theinvention, the ion sources 176, 190 may be moved in concert to remainaimed at a single desired spot on the sample 172, or may be movedindependently.

As described above with respect to a single FIB embodiment mountednormal to the sample 172, the intensity of the secondary electronemission in polycrystalline samples can captured by an electron detector180 as the ion beams 178, 188 are scanned across the sample 172.Accordingly, an ion beam normal to the surface, such as ion beam 178,and on axis with respect to the channeling directions of the sample 172can be used to image grain location. In addition, an ion beam not normalto the wafer surface, such as ion beam 188, and off axis with respect tothe channeling directions of the sample 172, can be used to image grainsize. Therefore, both grain size and grain location of a wafer can beimaged using, for example, a pair of FIB's, to provide inline analysisof a wafer process that can be used to control both a metal depositionprocess and a CMP process.

An additional feature of having two ion beams is that one ion beam 178,positioned parallel to a normal to the sample surface, may be used asone reference. The other ion beam, 188, which is not aligned with thenormal to the sample surface, can be used to determine a secondarycontrast intensity. Then, by rotating the sample about ion beam 178, theion beam 188 can be used to collect a series of contrast intensityimages which collectively can be used, along with crystal symmetry data,to reconstruct the discrete crystallographic makeup of the samplesurface. This method my enable the determination of the samplecrystallography without the need for a channeling reference, such as acrystal reference, to set the contrast intensities.

D. Combination SEM/FIB

Referring now to FIG. 24, an inline metrology element 200 combining andelectron source 230 and ion source 216 for scanning a crystalline sample212, will now be described. The metrology element 210 includes a sampleholder 214 for holding the sample 212 at a glancing angle θ1 to an ionbeam 218 and glancing angle θ2 to an electron source 230. In an aspectof the invention, the glancing angle θ1 is 90 degrees from the face ofthe crystalline sample 212. In another embodiment, the glancing angle θ1may be an angle between 0 and 90 degrees. The ion beam 218 is generatedby an ion source 216, such as a focused ion beam (FIB). A scanningactuator 224 is provided for controlling relative movement between theion beam 218 and the crystalline sample 212 on the sample holder 214.The scanning actuator 224 is controllable for directing the ion beam 218at selected areas of the crystalline sample 212. In other words, thescanning actuator 224 may control movement of the ion source 216 to movethe ion beam 218 relative to the sample 212 on the sample holder 214, orthe scanning actuator may control movement of the sample holder relativeto the electron beam, or both. An electron detector 220 is provided todetect electrons 226 emitted from the surface of the sample 212 as theion source 216 scans the ion beam 218 across the sample 212. In anaspect of the invention, the electron detector 220 is mountedperpendicular to the ion beam 18 near the edge of sample as shown inFIG. 24.

The electron beam 228 is generated by an electron source 230. A scanningactuator 224 is provided for controlling relative movement between theelectron beam 228 and the crystalline sample 212 on the sample holder14. In an aspect of the invention, glancing angle θ2 is 20 degrees. Thescanning actuator 224 is controllable for directing the electron beam228 at the sample 212, for example, in a series of spaced apart pointsof the crystalline sample 212. In other words, the scanning actuator 224may control movement of the electron source 216 to move the electronbeam 218 relative to the sample 212 on the sample holder 214, or thescanning actuator may control movement of the sample holder relative tothe electron beam, or both.

An image processor 236 is provided to process images formed on aphosphor screen 232, e.g., by intensifying and/or amplifying the images.The image processor 236 may comprise a low light or charged coupleddevice (CCD) camera 234 to capture the images. The phosphor screen 232is mounted adjacent the sample holder 214 so that it is parallel to theincident electron beam 218. Diffracted electrons from the sample 212form images on the phosphor screen 232 as described with respect toFIG. 1. These images are known as Kikuchi diffraction patterns andinclude Kikuchi bands, which can be used to determine thecrystallographic grain orientation at a point within a scan area of thesample 212. In addition, another image processor 221 may be provided toprocess contrast images received by the electron detector 220, e.g., byintensifying and/or amplifying the images. Once an image has beenprocessed for a specific area, a controller 222 provides a controlsignal to the scanning actuator 224 to move the ion beams 218, or thesample holder 214, to another desired area of the sample 212. In anaspect of the invention, the ion source 216 and electron source 230 maybe moved in concert to remain aimed at a desired single spot on thesample 212, or moved independently.

Accordingly, by using both an electron source 230 and an ion source 216in a dual configuration, efficient crystallographic inline analysis canbe performed. For example, the ion beam can be used for grainidentification and an electron beam can be used in point diffractionmode to determine orientation in a crystalline. In an aspect of theinvention, the ion beam can be used to grossly determine grain boundarylocations, while the electron beam 228 can be used in analytical mode iffiner crystallographic analysis, such as by using diffraction patterns,is required. For example, a sample of aluminum with a preferredorientation of (111) could be analyzed with an off-axis channelingmeasurement (that is, with an angle of, for example, 5 degrees betweenthe incident ion beam and the surface normal or θ2 equal to 90 degrees).In an off axis configuration, the grains would yield different contrastintensities. From an ion channeling contrast image, the grain boundariesmay be identified and the individual grains determined. Then, the ionbeam can be disengaged, and the electron beam may take a diffractionmeasurement from the center of each of the identified grains.Accordingly, both morphological information from the channeling contrastimage, and discrete crystallographic information from the electron beammeasurement, can be provided. Combining the two data sources can enablea full crystallographic characterization of the sample surface.

In another aspect of the invention, the off-axis measurement techniquedescribed above can be used to center, or provide a reference for, anon-axis, (θ1 equal to 90 degrees) method of ion channeling contrastdetermination. Therefore, the two beam method may be used to set theparameters and provide a reference for the single ion beam measurement.In practice, the first sample can be solved discretely by off-axis grainidentification, followed by on-axis discrete solving of thecrystallographic information. The sample may then be measured using theon-axis method with the off-axis method providing reference. Once areference is determined and contrast levels set for the differentcrystallographic directions, the rest of the sample set may only requirethe use of single ion beam for crystallography. This allows increasingthroughput of the sample sets while still allowing for a fullcrystallographic characterization of the sample surfaces.

In another aspect of this invention, a full determination of themorphology and crystallography can be performed at each cross sectionalplane through the sample followed by a three dimensionalcrystallographic reconstruction done.

III. FIB Crystal Standard

In ion beam imaging techniques, setting up a contrast scale using trialand error techniques can be problematic. In particular, centering ascale of contrast for a secondary electron emission image fordetermining crystallographic orientations in a sample can be difficultto achieve. The inventors of the present invention have innovativelyrealized that by creating a dual orientation fiber texture metrologystandard for providing a reference to set a contrast scale in ionimaging. For example, a bicrystal with grain boundaries specific to therotation angle boundaries between the two orientations can be used as astandard.

FIG. 25 depicts a vertical Bridgman vacuum furnace system 250 forgrowing a bicrystal according to the invention. FIG. 26 depicts agraphite mold 252 having a single nucleation site for growing a puresingle crystal of random orientation. FIG. 27 shows a seed crystal 254that can be extracted at a misorientation angle from a pure singlecrystal 256. As depicted in FIG. 27, the seed crystal 254 can be cutfrom the first crystal 256 of random orientation grown in the mold 252,so that the orientation of the seed crystal 256 incorporates half amisorientation angle rotated about the 001 axis. The seed crystal 254can then be ground and polished at a reference face so that the normalwith respect to the 001 axis is half the misorientation angle. FIG. 28depicts a graphite mold 258 to form a seed grown crystal from a puresingle seed crystal. The seed crystal 256 is placed in another graphitemold 258 and a seed grown crystal, having a specific orientation, isgrown. FIG. 30 depicts the stages of cutting a seed grown crystal 260into two bicrystal seeds halves 262, 264. As shown in FIG. 29, the seedgrown crystal 260 is cut into two bicrystal seeds halves 262, 264 in adirection parallel to the growth direction, one half 264 is rotated 180degrees and oriented with respect the other half 262 so that asymmetrical twist grain boundary separates the two crystals halves 262,264.

FIG. 30 depicts a graphite bicrystal mold 266 for forming a bicrystalfrom bicrystal seeds halves 262, 264. As shown in FIG. 30, the graphitebicrystal mold 266 accommodates a graphite sleeve 268 that keepsbicrystal seeds halves 262, 264 separated during the growing process andenforces the correct orientation for each bicrystal seeds halves 262,264. The resulting bicrystal exhibits a specific grain boundary.

The bicrystal misorientation can be characterized by using BKD methodsas described previously. The patterns can be indexed about theorientations of each crystal of the bicrystal determined. Themisorientation, if then described as a rotation of one crystals about acommon axis, that brings into coincidence with the second crystalaxis/angle. The calculation for misorientation can be written in termsof matrices:[X]1=[R][X]2  [6]

In the above equation, [X]1 and [X]2 are the normalized orientationvectors for component crystals 1 and 2, (for example, crystals 262 and264) respectively, of the bicrystal and [R] is the rotation matrix. Fromthe rotation matrix, the axis/angle pair can be calculated as follows,wherein angle of rotation:q=cos−1[0.5(R11+R22+R33−1)].  [7]

The rotation axis (hkl) is:h=(R32−R23)/(2 sinq);k=(R13−R31)/(2 sinq);andi=(R21−R12)/(2 sinq).  [9]

Once the bicrystal is grown, the bicrystal can be used as a standard tocorrelate the contrast level with the grains imaged by channelingcontrast. For example, as shown, in the, appropriated scaled contrastlevels reveal light areas 272 characteristic of 114 grain orientationsand dark areas 270 characteristic of 110 grain orientations.

IV. Methods for Inline Metrology

As described above with respect the use of an SEM, several novelvariable scan methods can be employed to monitor a deposition process,including statistical crystallographic orientation, statistical grainsize, and electron line differential to determine boundaries incrystalline substrates. In addition, a novel ion channel technique, asdescribed previously with respect to the use of an FIB, can be used todetermine single fiber texture strength, dual fiber texture strength,area fractions, area fraction v. depth through a film, reconstruction ofCMP removal rate curves from orientation data, and crystallographicchanges versus depth profile. In the previously described dual ionbeam/electron beam device, a novel method of channeling contrast ofgrain locations and point electron diffraction for orientation, and ionchanneling for fast inline methodology of problem identification and aslower analytical mode for detailed analysis was described. In addition,information derived from such metrology can be used to provide controlparameters to other processes such as a CMP process and a metaldeposition process. Other methods for process monitoring and control ofa semiconductor fabrication process will now be described.

A. Roughness Correlated Reflectivity

It is known that polycrystalline materials having preferred crystalplane orientations grow at different rates for different orientations.As polycrystalline material begins to nucleate and grow on a samplesurface, a characteristic of the material known as the Gibbs surfaceenergy defines the rate at which a crystalline surface will grow. TheGibbs surface energy is different for different orientations as theorientation of the surface defines the angle of the orbital for binding.For a surface that has a tendency to nucleate and grow in a singledirection, such as aluminum, the surface energy with respect to aspecific planar location should be relatively equivalent resulting in afairly uniform surface growth rate. Fro poly crystalline materials thatnucleate out and grow in different orientations, such as tungsten,different surface energies resulting in different growth rates for eachof the different polycrystalline orientations. This differential growthrate effectively creates surface roughness that can be monitored througha variety of monitoring techniques such as reflectivity, SEM analysis,and atomic force microscopy.

FIG. 33A is an inverse pole map of a relatively rough tungsten sampleimaged using electron beam backscatter. FIG. 33B is an inverse pole mapof a relatively smooth tungsten sample imaged using electron beambackscatter. As shown in FIG. 32B, a decreased roughness can becorrelated to an increased fraction 274 of 110 fiber texture oftungsten.

FIG. 33A is a reflectivity image of a relatively rough tungsten samplehaving an inset area fraction legend. FIG. 34B is reflectivity image ofa relatively smooth tungsten sample having an inset area fractionlegend. As shown by visual comparison of the reflectivity images andcorresponding area fraction legend, in increase of the area fraction 114textured orientation (from 0.02 in FIG. 33A to 0.14 in FIG. 33B),decreases the reflectivity that is caused by an increase in theroughness of the film.

B. Uniform Grain Removal Rate Via Differential Dwell Time

It is known that an FIB tool utilizing a focused beam of ions (typicallygallium) can remove material from a surface due to the collision cascadeof the incident ion beam. Due to channeling effects in polycrystallinestructures, certain orientations with respect to the incident ion beamcan mill faster or slower then other orientations, thereby causing asurface roughness or non-uniform removal rate during the millingoperation. Ion beam removal of a material is one way to either preparesamples or remove material. Accordingly, an FIB can be used forsemiconductor defect review, transmission electron beam and scanningtransmission electron beam sample preparation and potential forlocalized metrology.

Previous attempts to solve the problem of non-uniformity of removal ratebetween grain structures such as channels has been to rotate the samplewith respect to the incident beam angle. However, it is difficult torotate a sample/wafer while maintaining the focus on the surface tocontrol the milling. This technique has been more generally used forgross ion milling whereby the area of removal must be smooth and theorientation is not known or not known to vary with respect to alocalized area.

The inventors have innovatively realized that by characterizingdifferential removal rate with respect to orientation and channelingcontrast, the dwell time can be adjusted to compensate for thenon-uniform removal rate. Accordingly, polycrystalline materials wherebythere is a known or unknown orientation can be milled at a uniform rate.

In recent years, focused ion beam (FIB) instruments have becomeextremely useful in the microelectronics industry. One of the criticalapplications of FIB instruments is as a specimen preparation tool forsubsequent analysis in scanning electron microscopy (SEM), transmissionelectron microscopy (TEM), scanning transmission electron microscopy(STEM), secondary ion mass spectrometry (SIMS), and scanning augermicroscopy (SAM). Because of the ubiquitous use of Si-based integratedcircuits (IC's) and the push toward using Cu in IC metallizations,interest has been directed toward the FIB milling properties of Si andCu. It is well known that Si exhibits exceptional FIB millingproperties, while milling of Cu has been problematic.

An FIB instrument utilizes a finely focused ion beam from, for example,a Ga liquid metal ion source (LMIS) to perform imaging and millingoperations. The interaction of the finely focused ion beam with thetarget material will produce the ejection of secondary electrons,secondary ions, and secondary neutrals. The ions and neutrals can beejected as individual atoms, molecules, or clusters. The imagingcapability of the FIB allows the use of either the secondary electronsor the secondary ions for image formation. Milling operations areachieved through site specific sputtering (as described previously withrespect to an FIB inline metrology element) of the target material asdescribed previously with respect to an FIB inline metrology element.

Another particularly interesting capability of the FIB is that itproduces ion channeling contrast in the secondary electron images forpolycrystalline samples. Ion channeling contrast occurs because thesecondary electron yield varies as a function of crystallographicorientation within the sample. Channeling can occur when acrystallographic axis of a particular grain is aligned with the incidention beam. As a result, that grain will appear darker due in an electrondetector image due to a decrease in the number of secondary electronsthat are produced.

It has been well established that the sputtering yield is a function ofcrystallographic orientation. As the ion beam becomes incident in achanneling direction, the sputtering yield will decrease. The mainreason for the decrease in the sputtering yield is that the channeledions undergo mostly electronic energy losses as opposed to nuclearenergy losses and are able to penetrate deeper into the crystal lattice.The deeper penetration and the lower probability of nuclear collisionsnear the surface extremely limits the probability that the ion willcause a collision cascade that will contribute to the sputtering ofsurface atoms.

Using a Lindhard-Onderdelinden approach for mono-crystalline sputtering,the channeling directions and critical angles are calculated for 30 keVGa+ into Cu using the following seven equations. The channeledsputtering yield Yuvw is related to the amorphous sputtering yieldYamorph with the non-channeled fraction χuvw and a fitting parameterηhkl as shown in Equation 1.^(Y) ^(uvw) ^(=η) ^(nkl) ^(χ) ^(uvw) ^(Y) ^(amorph)   (1)The amorphous sputtering yield Yamorph is dependent on the angle ofincidence θ and the energy of the incident ion E. The non-channeledfraction χuvw is just the statistical fraction of the beam thatcontributes to sputtering in the axial channeling direction and isdependent on the critical channeling angle Ψc and the incident ionenergy E. The fitting parameter ηhkl will be assumed as unity in orderto analyze just the channeling effects. According to theLindhard-Onderdelinden approach, the non-channeling fraction at normalincidence χuvw can be calculated using the Thomas-Fermi potential forthe ion-atom interaction as shown in Equation 2. $\begin{matrix}{\chi_{uvw}^{o} = {\pi\quad{{Nt}_{uvw}^{3/2}\lbrack \frac{3A^{2}Z_{1}{Z_{2}( \frac{{\mathbb{e}}^{2}}{4{\pi ɛ}_{o}} )}}{E} \rbrack}^{1/2}}} & (2)\end{matrix}$The distance between atom positions along the index direction [uvw] istuvw. The elemental charge e is 1.60×10-19 C and permittivity constantεo is 8.85×10-12 C2/N*m2. The non-channeled fraction depends on both theatomic density N and the atomic number Z2 of the target material, theatomic number Z1 and energy E of the incident ion, and the Thomas-Fermiscreening length A shown in Equation 3. (Note again that this modelneglects the effects of planar channeling.) $\begin{matrix}{A = \frac{( \frac{9\quad\pi^{2}}{128} )^{1/3}a_{o}}{( {Z_{1}^{2/3} + Z_{2}^{2/3}} )^{1/2}}} & (3)\end{matrix}$The Thomas-Fermi screening length A depends on the atomic number of boththe incident ion and the target material Z1 and Z2 and the Bohr radiusao. $\begin{matrix}{a_{o} = {\frac{\eta^{2}}{m_{e}\frac{{\mathbb{e}}^{2}}{4{\pi ɛ}_{o}}} = {0.529177Å}}} & (4)\end{matrix}$Plank's constant divided by 2πη is 1.05×10-34 J*s and the mass of theelectron me is 9.11×10-31 kg. With the establishment of thenon-channeled fraction at normal incidence χouvw, the channelingdirections can be calculated for a given energetic incident ion and atarget material. Next, the angular width of the channeling directions,called the critical angle ψc, can be calculated. $\begin{matrix}{\psi_{c} = \lbrack \frac{3A^{2}Z_{1}{Z_{2}( \frac{{\mathbb{e}}^{2}}{4{\pi ɛ}_{o}} )}}{{Et}_{uvw}^{3}} \rbrack^{1/4}} & (5) \\{{E < E_{1}} = \frac{2Z_{1}{Z_{2}( \frac{{\mathbb{e}}^{2}}{4{\pi ɛ}_{o}} )}t_{uvw}}{A^{2}}} & (6)\end{matrix}$Equation 5 is valid as long as the energy of the incident ion is lessthan E1, which is the upper limit for Lindhard's low energyapproximation according to Equation 6. The calculated upper limit forthe case of 30 keV Ga into Cu is ˜5.8 MeV. As the ion beam deviates fromthe direct channeling direction, the non-channeled fraction willincrease toward unity as channeling becomes less statistically possible.The polar angle resolved non-channeled fraction is then denoted as χuvwas shown in Equation 7. $\begin{matrix}{\chi_{uvw} = \frac{\chi_{uvw}^{o}}{1 - {( {1 - \chi_{uvw}^{o}} )( \frac{\psi}{f\quad\psi_{c}} )^{2}}}} & (7)\end{matrix}$

The polar angle from normal incidence along a channeling direction [uvw]is ψ. The fitting parameter f is included in order to accurately fit themodel to experimental data. When the ion channeling contrast across theboundary is not uniform ,the result is differential sputtering as wellas trench wall sloping from re-deposition on half of the specimen. Sincethe TEM specimen must be a uniform thickness, a modified millingtechnique must be employed in order to achieve a quality TEM Lift-Outspecimen. Using the ion channeling contrast as a guide, the sample canbe tilted a few degrees until the ion channeling contrast across theboundary is uniform. The differential sputtering is eliminated and theeffects of re-deposition have been reduced in order to achieve thedesired uniform thickness of the specimen. Therefore:$Y = {{4\frac{C}{X}} - {3Y_{amorph}}}$ C = contrast  levelX = scaling  factorwhere X is defined as the value at which$X = \frac{C_{0}}{Y_{uvw}( {\theta = 0} )}$Y_(amorph)=amorphous sputtering yield at normal incidence

Accordingly, because sputtering yield can be tied to ion channelingcontrast, a mechanism for compensation can be developed whereby thedepth of material removed is a function of the sputtering yield and thedwell time at a particular location For example, within the sampledifferent grain structures can readily be seen. The twin component ofthe copper grains is also well visualized. The image in FIG. 34 iscreated by monitoring the secondary electron output of a primaryincident ion beam. Accordingly, the image in FIG. 34 is essentially anion channeling contrast map of the sample surface.

The incident ion beam can be instructed to dwell (the length of time theprimary ion beam spends at any particular location) at each point alonga scan. A differential dwell time can be used that is calculated througha scaling function of the ion channeling contrast. For example, for theselected region of copper shown in the image of FIG. 35 a singular graincan be seen and the sigma three FCC annealing twins can be seen as theregions of a darker contrast. For the areas that are darker, the ionbeam can be directed to dwell a longer time since thecontrast-sputtering yield equations dictate that for darker ionchanneling contrasts there is less sputtering yield. For example, FIG.37A shows an image of a copper bicrystal whereby the difference incontrast levels between the right side 276 of the crystal and the leftside 278 of the crystal is a grain boundary 280. When the channelingcontrast, a dramatic difference in the depth of the cuts is evident.

When the contrasts are then equalized as in the image of FIG. 36B,before the milling procedure, an even removal rate across the grainboundary despite the different sides of the boundary have appreciablydifferent orientations. Accordingly, by using ion channeling contrast,differential dwell times can be programmed for different area tomaintain a constant sputtering yield over the polycrystalline material.

C. CMP Processing

CMP processing may be controlled using the embodiments described herein.The disclosed methods may be used to maintain an equal plane of materialremoval though a sample of differing intensity contrasts or removalrates. Once it is determined that the material may be removed in aplanar manner, with an effective method of determining thecrystallography at each plane, a three dimensional reconstruction of thecrystallographic makeup of a sample surface may be obtained. The methodmay also include determining when to cease removal of the material basedon the orientations of crystals of the removal surface.

While the preferred embodiments of the present invention have been shownand described herein, it will be obvious that such embodiments areprovided by way of example only. Accordingly, it is intended that theinvention be limited only by the spirit and scope of the appendedclaims.

1. A system for crystallography comprising: a sample holder for holdinga crystalline sample for characterization of a sample area; an electronsource for generating an electron beam; a scanning actuator forcontrolling the relative movement between the electron beam and thecrystalline sample, the scanning actuator being controllable fordirecting the electron beam at a series of spaced apart points withinthe sample area; a first processing system for generatingcrystallographic data based upon electron diffraction from thecrystalline sample; a second processing system configured fordetermining whether sufficient data have been acquired to characterizethe sample area; and a controller for controlling the scanning actuatorto space the points apart such that acquired data is representative of adifferent grains within the crystalline sample.
 2. The system of claim1, further comprising a third processing system for determining grainsizes in the sample area by counting grain boundaries intersectingconcentric circles of spaced apart points.
 3. The system of claim 1,further comprising a third processing system for determining grain sizesin the sample area by counting grain boundaries along a series of linesof spaced apart points.
 4. The system of claim 1, further comprising afeed-forward loop for providing control parameters, based upon thecrystallographic data, to a chemical-mechanical polishing station. 5.The system of claim 1, further comprising a feedback loop for providingcontrol parameters to a deposition station based upon thecrystallographic data.
 6. The system of claim 1, wherein the electronbeam is positioned to intercept the sample at an angle of approximately20 degrees.
 7. The system of claim 1, further comprising a crystallinestandard for providing a reference to the processing system.
 8. A systemfor inline crystallographic metrology comprising: a sample holder forholding a crystalline sample; a first ion source for generating a firstion beam; a scanning actuator for controlling the relative movementbetween the first ion beam and the crystalline sample, the scanningactuator being controllable for directing the first ion beam at desiredareas of the crystalline sample; an electron detector for detectingsecondary electrons emitted from the crystalline sample; a firstprocessing system for creating a contrast intensity image based uponsecondary electron emissions from the crystalline sample; a secondprocessing system programmed to provide crystallographic informationbased on the contrast image intensity data; and a controller forcontrolling the scanning actuator for scanning the first ion beam. 9.The system of claim 8, further comprising a feed-forward loop forproviding control parameters to a chemical-mechanical polishing stationbased upon acquired data.
 10. The system of claim 8, further comprisinga feedback loop for providing control parameters to a deposition stationbased upon acquired data.
 11. The system of claim 8, wherein the firstion beam is positioned to intercept the crystalline sample at an angleof approximately 90 degrees.
 12. The system of claim 8, furthercomprising a crystalline standard for providing a reference to theprocessing system.
 13. The system of claim 8, further comprising asecond ion source for generating a second ion beam, the second ionsource controllable by the scanning actuator.
 14. The system of claim13, wherein the second ion beam is positioned to intercept thecrystalline sample at angle of between 0 degrees and 90 degrees.
 15. Asystem for inline crystallographic metrology comprising: a sample holderfor holding a crystalline sample; a first ion source for generating afirst ion beam; an electron source for generating an electron beam; ascanning actuator for controlling the relative movement between thefirst ion beam, the electron beam, and the crystalline sample, thescanning actuator being controllable for directing the first ion beam atdesired areas of the crystalline sample and for directing the electronbeam at a series of points within the sample area; an electron detectorfor detecting secondary electron emissions from the crystalline sample;a first processing system for creating a contrast intensity image basedupon secondary electron emissions from the crystalline sample andgenerating crystallographic data based upon electron diffraction fromthe crystalline sample; a second processing system programmed to providecrystallographic information based on the contrast image intensity dataand configured for determining whether sufficient data have beenacquired to characterize the sample area; and a controller forcontrolling the scanning actuator to direct the first ion beam atdesired areas such that each ion channeling image is representative ofchanneling directions within the crystalline sample and to space thepoints apart such that acquired data is representative of a differentgrains within the crystalline sample.
 16. The system of claim 15,further comprising a feed-forward loop for providing control parametersto a chemical-mechanical polishing station based upon acquired data. 17.The system of claim 15, further comprising a feedback loop for providingcontrol parameters to a deposition station based upon acquired data. 18.The system of claim 16, wherein the electron beam is positioned tointercept the sample at an angle of approximately 20 degrees.
 19. Thesystem of claim 16, further comprising a crystalline standard forproviding a electron diffraction reference to the processing system. 20.The system of claim 16, further comprising a crystalline standard forproviding an ion channeling reference to the processing system.
 22. Thesystem of claim 16, wherein the ion beam is positioned to intercept thecrystalline sample at an angle of approximately 90 degrees.
 23. Acrystallographic standard comprising crystalline orientations in aprimary channeling direction and a primary de-channeling direction forestablishing a contrast setting for ion channeling analysis ofcrystalline sample.
 24. A crystallographic standard for ion channelinganalysis of a crystalline sample having a first primary channelingdirection and a second primary channeling direction, the standardcomprising a bicrystal having respective boundary rotation anglesdefined by the first primary channeling direction and the second primarychanneling direction.
 25. A crystallographic standard for ion channelinganalysis of a crystalline sample comprising a plurality of differentfiber texture components for setting pass/fail rotation allowances. 26.A crystallographic standard for ion channeling analysis of a crystallinesample comprising a plurality of different fiber texture components forsetting pass/fail rotation allowances.
 27. A crystallographic standardfor off axis ion channeling analysis of a crystalline sample comprisinga common axial crystal direction.
 28. The crystallographic standard ofclaim 27, further comprising a random rolling component.
 29. Thecrystallographic standard of claim 27, further comprising a fixedrolling component.
 30. A method for determining crystallography of bulkcrystal sample comprising: providing a sample holder for holding acrystalline sample for characterization of a sample area; generating anelectron beam; controlling the relative movement between the electronbeam and the crystalline sample to direct the electron beam at a seriesof spaced apart points within the sample area; generatingcrystallographic data based upon electron diffraction from thecrystalline sample; determining whether sufficient data have beenacquired to characterize the sample area; and spacing the points apartsuch that acquired data is representative of a different grain withinthe crystalline sample.
 31. The method of claim 30, further comprisingdetermining grain sizes in the sample area by counting grain boundariesintersecting concentric circles of spaced apart points.
 32. The systemof claim 30, further comprising determining grain sizes in the samplearea by counting grain boundaries along a series of lines of spacedapart points.
 33. The system of claim 30, further comprising providingfeed-forward control parameters, based upon the crystallographic data,to a chemical-mechanical polishing station.
 34. The method of claim 30,further comprising controlling polarity of a chemical-mechanicalpolishing slurry to modify relative material removal rates fromdifferent crystalline planes of a crystalline sample to allow consistentendpoint prediction of the chemical-mechanical polishing process. 35.The method of claim 30, further comprising providing feedback controlparameters to a deposition process based upon the acquired data.
 36. Themethod of claim 30, further comprising positioning the electron beam tointercept the sample at an angle of approximately 20 degrees.
 37. Themethod of claim 30, further comprising setting data processingparameters based on a crystallographic standard.
 38. A method fordetermining crystallography of bulk crystal sample comprising: providinga sample holder for holding a crystalline sample; generating a first ionbeam; controlling the relative movement between the first ion beam andthe crystalline sample, for directing the first ion beam at desiredareas of the crystalline sample; detecting secondary electrons emittedfrom the crystalline sample; creating a contrast intensity image basedupon secondary electron emissions from the crystalline sample; providingcrystallographic information based on the contrast image intensity data;and controlling the scanning actuator for scanning the first ion beam.39. The method of claim 38, further comprising providing controlparameters, based upon processed emission data, to a chemical-mechanicalpolishing process.
 40. The method of claim 39, further comprisingcontrolling polarity of a chemical-mechanical polishing slurry to modifyrelative material removal rates from different crystalline planes of acrystalline sample to allow consistent endpoint prediction of thechemical-mechanical polishing process.
 41. The method of claim 38,further comprising providing control parameters, based upon processedemission data, to a deposition process.
 42. The method of claim 38,further comprising positioning the first ion beam to intercept thecrystalline sample at an angle of approximately 90 degrees.
 43. Themethod of claim 38, further comprising directing the first ion beam atdesired areas of the crystalline sample in a desired direction such thatprocessed emission data is representative of channeling directionswithin the crystalline sample.
 44. The method of claim 43, furthercomprising rotating the sample about an axis of the incident first ionbeam to align the first ion beam with a channeling direction of thesample.
 45. The method of claim 38, further comprising setting emissiondata processing parameters based on a crystallographic standard.
 46. Themethod of claim 38, further comprising providing a second ion source forgenerating a second ion beam.
 47. The method of claim 46, furthercomprising positioning the second ion beam to intercept the crystallinesample at angle of between 0 degrees and 90 degrees.
 48. The method ofclaim 46, further comprising determining a reference contrast settingby: rotating the crystalline sample about the longitudinal axis of thefirst ion beam; collecting a series of contrast intensity imagesgenerated by the first and second ion beams; and comparing the collectedcontrast intensity images to reconstruct the crystallography of thecrystalline sample.
 49. The method of claim 38, further comprising usingcrystallographic information to determine crystallographic parameters ofthe bulk crystal sample selected from the group consisting of singlefiber texture strength, dual fiber texture strength, area fraction, areafraction versus. depth through a film, reconstruction of CMP removalrate curves from orientation data, and crystallographic changes versusdepth profile.
 50. A method for determining crystallography of bulkcrystal sample comprising: providing a sample holder for holding acrystalline sample; generating a first ion beam; generating an electronbeam; controlling the relative movement between the first ion beam, theelectron beam, and the crystalline sample for directing the first ionbeam at desired areas of the crystalline sample and for directing theelectron beam at a series of points within the sample area; detectingsecondary electron emissions from the crystalline sample; creating acontrast intensity image based upon secondary electron emissions fromthe crystalline sample and generating crystallographic data based uponelectron diffraction from the crystalline sample; providingcrystallographic information based on the contrast image intensity dataand configured for determining whether sufficient data have beenacquired to characterize the sample area; and controlling the scanningactuator to direct the first ion beam at desired areas such that eachion channeling image is representative of channeling directions withinthe crystalline sample and spacing the points apart such that acquireddata is representative of a different grains within the crystallinesample.
 51. The method of claim 50, further comprising spacing thepoints apart sufficient distances such that diffraction data receivedfrom the points emanates from different grains of the crystallinesample.
 52. The method of claim 50, further comprising directing thefirst ion beam at desired areas of the crystalline sample in a desireddirection such that emission data is representative of channelingdirections within the crystalline sample.
 53. The method of claim 52,further comprising rotating the sample about an axis of the incidentfirst ion beam to align the first ion beam with a channeling directionof the sample.
 54. The method of claim 50, further comprising providingcontrol parameters, based upon processed emission data and diffractiondata, to a chemical-mechanical polishing process.
 55. The method ofclaim 54, further comprising controlling polarity of achemical-mechanical polishing slurry to modify relative material removalrates from different crystalline planes of a crystalline sample to allowconsistent endpoint prediction of the chemical-mechanical polishingprocess.
 56. The method of claim 50, further comprising providingcontrol parameters, based upon processed emission data and diffractiondata, to a deposition process.
 57. The method of claim 50, furthercomprising positioning the electron beam to intercept the sample at anangle of approximately 20 degrees.
 58. The method of claim 50, furthercomprising further comprising setting data processing parameters basedon a crystallographic standard.
 59. The method of claim 50, furthercomprising positioning the first ion beam to intercept the crystallinesample at an angle of approximately 90 degrees.
 60. The method of claim50, further comprising using crystallographic information to determinecrystallographic parameters of the bulk crystal sample selected from thegroup consisting of single fiber texture strength, dual fiber texturestrength, area fraction, area fraction versus depth through a film,reconstruction of CMP removal rate curves from orientation data, andcrystallographic changes versus depth profile.